【摘要】：噪聲源識(shí)別是進(jìn)行噪聲抑制和降噪的前提條件，由于高速列車工作狀態(tài)和速度的不同而導(dǎo)致高鐵運(yùn)動(dòng)噪聲源頻率、強(qiáng)弱等特性發(fā)生變化，給外部噪聲源的空間指向性建模、測(cè)試分析帶來很大的困難。經(jīng)典“延時(shí)-累加”波束形成方法在識(shí)別運(yùn)動(dòng)聲源中存在不足，無法克服運(yùn)動(dòng)噪聲源存在的多普勒效應(yīng)。本課題對(duì)運(yùn)動(dòng)噪聲源定位的理論算法、陣列設(shè)計(jì)、建模仿真進(jìn)行了研究，具有重要的實(shí)用參考價(jià)值和廣闊應(yīng)用前景。 本文詳細(xì)分析和總結(jié)國內(nèi)外研究經(jīng)驗(yàn)，以經(jīng)典波束形成和運(yùn)動(dòng)聲源輻射理論為基礎(chǔ)，以運(yùn)動(dòng)噪聲源識(shí)別技術(shù)為主，采用理論研究與仿真分析相結(jié)合的方法，對(duì)運(yùn)動(dòng)噪聲源識(shí)別和聲場(chǎng)重建技術(shù)進(jìn)行研究。論文的主要研究內(nèi)容包括以下幾個(gè)部分： （1）運(yùn)動(dòng)聲源輻射問題。在經(jīng)典“延時(shí)-累加”波束形成對(duì)靜止聲源識(shí)別的基礎(chǔ)上提出該方法在運(yùn)動(dòng)聲源識(shí)別中存在的不足，闡述了運(yùn)動(dòng)聲源輻射的相關(guān)問題，給出了運(yùn)動(dòng)聲場(chǎng)輻射數(shù)學(xué)描述。 （2）運(yùn)動(dòng)聲源多普勒效應(yīng)處理。分析運(yùn)動(dòng)列車陣列信號(hào)基本理論，建立運(yùn)動(dòng)聲源點(diǎn)與測(cè)量點(diǎn)幾何模型。針對(duì)常規(guī)運(yùn)動(dòng)聲源識(shí)別方法對(duì)幅度和頻率進(jìn)行簡(jiǎn)單校正，波數(shù)域仍然存在多普勒效應(yīng)和簡(jiǎn)化模型存在定位誤差的情況，本論文提出基于非簡(jiǎn)化模型的聲源測(cè)量方法，通過微積分方法建立消除多普勒效應(yīng)的運(yùn)動(dòng)聲源數(shù)學(xué)模型，該模型為一種無損精度數(shù)學(xué)模型，并由此推導(dǎo)出基于非簡(jiǎn)化模型的運(yùn)動(dòng)聲源波束形成聲場(chǎng)重建公式。為最大程度提高系統(tǒng)的抗干擾能力，增強(qiáng)系統(tǒng)聲源指向的準(zhǔn)確性，采用去自相關(guān)項(xiàng)方法來降低降噪聲污染。 （3）麥克風(fēng)陣列性能研究。詳細(xì)分析噪聲信號(hào)頻率、陣列間距、陣元數(shù)目、陣列尺寸等陣列幾何參數(shù)對(duì)陣列分辨率等性能的重要影響。以十字陣、矩形陣、六角陣為對(duì)象，在陣列方向性、圓對(duì)稱性、角度分辨率等性能方面進(jìn)行定性和定量對(duì)比仿真與分析。 （4）波束形成算法性能研究。在方位譜估計(jì)的理論基礎(chǔ)上，給出了常規(guī)波束形成器、MVDR（Minimum Variance Distortionless Response）波束形成器、MUSIC(Multiple Signal Classification)波束形成器在兩聲源方位間隔Δθ=5°和Δθ=10°的掃描方位譜。通過信噪比分別為-5dB和5dB時(shí)分辨概率和方位間隔的關(guān)系以及輸入信噪比SNR的值在-30~30dB間變化時(shí)SINR與輸入SNR的關(guān)系對(duì)三種波束形成器穩(wěn)定性、陣列增益等性能進(jìn)行了對(duì)比研究并給出相關(guān)結(jié)論，為后續(xù)聲場(chǎng)重建仿真選擇算法提供依據(jù)。 （5）基于波束形成算法噪聲源識(shí)別聲場(chǎng)仿真。基于麥克風(fēng)陣列在噪聲源識(shí)別實(shí)際應(yīng)用中陣列布局、陣列選型等方面的相關(guān)結(jié)論和波束形成算法性能對(duì)比研究，，本文以MATLAB為工具，結(jié)合常規(guī)波束形成算法、MVDR波束形成算法分別對(duì)單頻聲源、多頻聲源、偶極子、分布源等聲源進(jìn)行基于非簡(jiǎn)化模型和簡(jiǎn)化模型的運(yùn)動(dòng)噪聲源識(shí)別聲場(chǎng)重建對(duì)比仿真。
[Abstract]:Noise source identification is a prerequisite for noise suppression and noise reduction. Because of the different operating state and speed of high-speed train, the frequency, intensity and other characteristics of high-speed train moving noise source are changed, and the spatial directivity of external noise source is modeled. Test analysis presents great difficulties. The classical "delay-cumulation" beamforming method can not overcome the Doppler effect of moving noise sources because of its shortcomings in identifying moving sound sources. In this paper, the theoretical algorithm, array design, modeling and simulation of moving noise source localization are studied, which has important practical reference value and broad application prospect. This paper analyzes and summarizes the domestic and foreign research experiences in detail, based on the classical beamforming and the theory of moving sound source radiation, and mainly based on the recognition technology of moving noise source, and adopts the method of combining theoretical research with simulation analysis. The identification of moving noise sources and the technique of sound field reconstruction are studied. The main contents of this paper are as follows: (1) the radiation of moving sound source. On the basis of the classical "time-delay cumulative" beamforming method for the recognition of static sound sources, the shortcomings of this method in the recognition of moving sound sources are proposed. The related problems of moving sound source radiation are expounded, and the mathematical description of moving sound field radiation is given. (2) Doppler effect processing of moving sound source. The basic theory of moving train array signal is analyzed and the geometric model of moving sound source point and measuring point is established. In view of the simple correction of amplitude and frequency by conventional moving sound source recognition methods, the Doppler effect in wavenumber domain and the localization error in simplified model are still existed. In this paper, a method of sound source measurement based on unsimplified model is proposed in this paper. The mathematical model of moving sound source for eliminating Doppler effect is established by means of calculus method. The model is a mathematical model with lossless accuracy and the formula of acoustic field reconstruction of moving sound source beamforming based on non-simplified model is derived. In order to improve the anti-interference ability of the system and enhance the accuracy of the sound source pointing, the method of de-autocorrelation is adopted to reduce the noise pollution. (3) performance of microphone array. The effects of array geometry parameters, such as noise signal frequency, array spacing, array number and array size, on array resolution are analyzed in detail. Taking cross array, rectangular array and hexagonal array as objects, qualitative and quantitative simulation and analysis of array directivity, circular symmetry and angle resolution are carried out. (4) performance of beamforming algorithm. Based on the theory of azimuth spectrum estimation, the scanning azimuth spectrum of conventional beamformer, MVDR (Minimum Variance Distortionless Response) beamformer, MUSIC (Multiple Signal Classification) beamformer between two sound source azimuth 螖 胃 = 5 擄and 螖 胃 = 10 擄is given. The stability of the three beamforming devices is obtained by the relationship between the resolution probability and the azimuth interval when the SNR is-5dB and 5dB, and the relation between SINR and the input SNR when the input SNR changes between-30~30dB and the signal to noise ratio (SNR). The performance of array gain is compared and the relevant conclusions are given, which provide the basis for the selection algorithm of subsequent acoustic field reconstruction simulation. (5) Acoustic field simulation based on beamforming algorithm. Based on the relative conclusions of array layout and array selection in the practical application of microphone array in noise source recognition and the comparative study of beamforming algorithm performance, this paper uses MATLAB as a tool and combines with conventional beamforming algorithm. MVDR beamforming algorithm is used to reconstruct the acoustic field of moving noise source recognition based on non-simplified model and simplified model, respectively, for single frequency sound source, multi-frequency sound source, dipole, distributed source and so on.
【學(xué)位授予單位】：中國計(jì)量學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U270.16;TB53

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